高职院校数学建模课程的改革研究

分析了高职院校数学建模课程教学中存在的主要问题,并提出相应的改革对策和建议。     

Application of the relevance vector machine to canal flow prediction in the Sevier River Basin

This work addresses management of water for irrigation in arid regions where significant delays between the time of order and the time of delivery present major difficulties. Motivated by improvements to water management that will be facilitated by an ability to predict water demand, it employs a data-driven approach to developing canal flow prediction models using the relevance vector machine (RVM), a probabilistic kernel-based learning machine. A search is performed across model attributes including input set, kernel scale parameter and model update scheme for models providing superior prediction capability using the RVM. Models are developed for two canals in the Sevier River Basin of southern Utah for prediction horizons of up to 5 days.     

Comparative evaluation of SCS-CN-inspired models in applications to classified datasets

One of the popular methods for estimating the depth of surface runoff for a given rainfall event is the Soil Conservation Service Curve Number (SCS-CN) method. Of late, several inconsistencies in its soil moisture accounting procedure have been pointed out by Michel et al. (2005), and a more rational procedure suggested. Recently, a modification incorporating an expression for estimation of initial soil moisture store level, a crucial parameter, was suggested by Sahu et al. (2007). The present study compares this modification with the original SCS-CN model and the other available variants on a large set of data of 76 small agricultural watersheds of the United States and finally suggests an improved model. The comprehensive comparison between these models reveals the proposed improvement to perform better than all other versions in all classified applications based on land use, soil type, combinations of land use and soil type, and precipitation regimes. A simplified version of the model is further suggested for practical applications.     

面向加工的轴流式水轮机叶片数字化建模方法研究

对轴流式水轮机叶片的数字化建模方法进行研究。从木模图提供的原始数据出发,结合叶片各组成曲面的型面特点和加工工艺要求,提出了一种用"类似流线"的描述方式来定义叶片的方法,并利用Unigraphics NX(软件构建了叶片空间曲面。叶片表面的光滑性检查和合理性检验结果验证了该方法的可行性,完整的叶片模型为叶片后续的数控加工奠定了基础。     

Predicting the occurrence of Atlantic bluefin tuna (Thunnus thynnus) larvae in the northern Gulf of Mexico: building a classification model from archival data

Although bluefin tuna are found throughout the Atlantic Ocean, spawning in the western Atlantic has been recorded predominantly in the Gulf of Mexico (GOM) in spring. Larval bluefin tuna abundances from the northern GOM are formulated into an index used to tune the adult stock assessment, and the variability of this index is currently high. This study investigated whether some of the variability in larval bluefin tuna abundances was related to environmental conditions, by defining associations between larval bluefin tuna catch locations, and a suite of environmental variables. We hypothesized that certain habitat types, as defined by environmental variables, would be more likely to contain bluefin tuna larvae. Favorable habitat for bluefin tuna larvae was defined using a classification tree approach. Habitat within the Loop Current was generally less favorable, as were warm‐core rings, and cooler waters on the continental shelf. The location and size of favorable habitat was highly variable among years, which was reflected in the locations of larval bluefin tuna catches. The model successfully placed bluefin tuna larvae in favorable habitat with nearly 90% accuracy, but many negative stations were also located within theoretically favorable habitat. The probability of collecting larval bluefin tuna in favorable habitat was nearly twice the probability of collecting bluefin tuna larvae across all habitats (35.5 versus 21.0%). This model is a useful addition to knowledge of larval bluefin tuna distributions; however, the incorporation of variables describing finer‐scale features, such as thermal fronts, may significantly improve the model’s predictive power.     

涡轮弯扭叶片的几何造型

现代涡轮叶片是弯曲、扭转、变截面复杂流线型面，一般无法解析表达，为满足叶片型面数控加工要求，针对给定若干叶片截面上型值点的情况，采用节点号参数化双三次Ｂ样条插值曲面对完整弯扭叶片进行三维造型，并提出考察造型型值点网格密度是否满足加工精度要求的判断准则，实际加工证明，此算法简洁，稳定，适合涡轮叶片加工的工程需要。     

关于灾害力学的思考

力学的发展与人类的生活、生产紧密相联。通过对人类抗灾防灾过程的回顾,提出了"软灾害"和"硬灾害"的概念。并通过对硬灾害致害机理的分析,介绍了灾害力学产生的背景、主要研究内容和研究方法,提出了灾害力学的控制方程。     

副猪嗜血杆菌S-核糖基高半胱氨酸酶基因序列分析与推导蛋白三维结构的分子模拟

利用PCR方法获得副猪嗜血杆菌S-核糖基高半胱氨酸酶(luxS)基因全长DNA,将PCR纯化产物与pMD18-T载体连接并转化E.coil DH5α菌株,重组阳性质粒测序并采用生物信息学软件对所推导的氨基酸序列进行三维结构分析。结果表明,该基因全长510bp,并与GenBank中登录的其他5株菌株luxS基因完整参考序列进行比较,同源性均在70%以上。用ExPAsy软件包预测了推导蛋白的特性,运用Swiss-PDB viewer软件的SWISS-Model处理器,并利用同源建模的思想建立HPS-luxS的三维结构。拉马钱德兰图证明,构建的luxS蛋白的空间结构是合理的。     

Egg production forecasting: Determining efficient modeling approaches

Several mathematical or statistical and artificial intelligence models were developed to compare egg production forecasts in commercial layers. Initial data for these models were collected from a comparative layer trial on commercial strains conducted at the Poultry Research Farms, Auburn University. Simulated data were produced to represent new scenarios by using means and SD of egg production of the 22 commercial strains. From the simulated data, random examples were generated for neural network training and testing for the weekly egg production prediction from wk 22 to 36. Three neural network architectures-back-propagation-3, Ward-5, and the general regression neural network-were compared for their efficiency to forecast egg production, along with other traditional models. The general regression neural network gave the best-fitting line, which almost overlapped with the commercial egg production data, with an R(2) of 0.71. The general regression neural network-predicted curve was compared with original egg production data, the average curves of white-shelled and brown-shelled strains, linear regression predictions, and the Gompertz nonlinear model. The general regression neural network was superior in all these comparisons and may be the model of choice if the initial overprediction is managed efficiently. In general, neural network models are efficient, are easy to use, require fewer data, and are practical under farm management conditions to forecast egg production.     

黑河流域出山径流模拟

国际水文科学协会已提出针对干旱半干旱区的"无资料地区的水文预测"项目。水已成为制约西北干旱半干旱区社会经济发展和生活改善的主要因素。预测全球变化和人类活动下的水循环过程和水资源变化格局是重要的。文中以黑河上游为研究区,应用IHACRES模型模拟黑河上游降水-径流过程。结果表明:IHACRES模型可以较好地模拟研究区的日出山径流量;率定期确定性系数达0.766,验证期确定性系数为0.71;确定黑河上游地区的基流指数为0.539;黑河上游经莺落峡水文站向中游输送水量为14.9亿m3,其中地表径流与基流分别占46.1%和53.9%;模型模拟受流域面积大及降雪等因素影响,使模拟精度降低;最后指出IHACRES模型适合缺乏资料地区水文模拟的研究。